# Attention-based Shape and Gait Representations Learning for Video-based Cloth-Changing Person Re-Identification

arXiv: https://arxiv.org/abs/2402.03716 · HTML: https://mlsanity.com/paper/2402.03716/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 50 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://mlsanity.com/paper/1804.01438/) (2018)
- [GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition](https://mlsanity.com/paper/2101.11228/) (2021)
- [BlazePose: On-device Real-time Body Pose tracking](https://mlsanity.com/paper/2006.10204/) (2020)
- [Clothes-Changing Person Re-identification with RGB Modality Only](https://mlsanity.com/paper/2204.06890/) (2022)
- [Person Re-identification by Contour Sketch under Moderate Clothing Change](https://mlsanity.com/paper/2002.02295/) (2020)
- [Long-Term Cloth-Changing Person Re-identification](https://mlsanity.com/paper/2005.12633/) (2020)
- [Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization](https://mlsanity.com/paper/2103.15537/) (2021)
- [Appearance-Preserving 3D Convolution for Video-based Person Re-identification](https://mlsanity.com/paper/2007.08434/) (2020)
- [When Person Re-identification Meets Changing Clothes](https://mlsanity.com/paper/2003.04070/) (2020)
- [Unsupervised Person Re-identification by Deep Learning Tracklet Association](https://mlsanity.com/paper/1809.02874/) (2018)
- [Watching You: Global-guided Reciprocal Learning for Video-based Person Re-identification](https://mlsanity.com/paper/2103.04337/) (2021)
- [Multi-scale 3D Convolution Network for Video Based Person Re-Identification](https://mlsanity.com/paper/1811.07468/) (2018)
- [Adaptive Graph Representation Learning for Video Person Re-identification](https://mlsanity.com/paper/1909.02240/) (2019)
- [Parameter-Efficient Person Re-identification in the 3D Space](https://mlsanity.com/paper/2006.04569/) (2020)
- [Spatially and Temporally Efficient Non-local Attention Network for Video-based Person Re-Identification](https://mlsanity.com/paper/1908.01683/) (2019)
- [Semantic-guided Pixel Sampling for Cloth-Changing Person Re-identification](https://mlsanity.com/paper/2107.11522/) (2021)
- [Person Re-Identification via Recurrent Feature Aggregation](https://mlsanity.com/paper/1701.06351/) (2017)
- [A Benchmark of Video-Based Clothes-Changing Person Re-Identification](https://mlsanity.com/paper/2211.11165/) (2022)
- [Building Footprint Extraction in Dense Areas using Super Resolution and Frame Field Learning](https://mlsanity.com/paper/2309.01656/) (2023)

## Built on by (most cited first, 3 of 3)

- [Data Quality Aware Approaches for Addressing Model Drift of Semantic Segmentation Models](https://mlsanity.com/paper/2402.07258/) (2024)
- [VReID-XFD: Video-based Person Re-identification at Extreme Far Distance Challenge Results](https://mlsanity.com/paper/2601.01312/) (2026)
- [CCPA: Long-term Person Re-Identification via Contrastive Clothing and Pose Augmentation](https://mlsanity.com/paper/2402.14454/) (2024)

## Abstract (arXiv)

Current state-of-the-art Video-based Person Re-Identification (Re-ID) primarily relies on appearance features extracted by deep learning models. These methods are not applicable for long-term analysis in real-world scenarios where persons have changed clothes, making appearance information unreliable. In this work, we deal with the practical problem of Video-based Cloth-Changing Person Re-ID (VCCRe-ID) by proposing "Attention-based Shape and Gait Representations Learning" (ASGL) for VCCRe-ID. Our ASGL framework improves Re-ID performance under clothing variations by learning clothing-invariant gait cues using a Spatial-Temporal Graph Attention Network (ST-GAT). Given the 3D-skeleton-based spatial-temporal graph, our proposed ST-GAT comprises multi-head attention modules, which are able to enhance the robustness of gait embeddings under viewpoint changes and occlusions. The ST-GAT amplifies the important motion ranges and reduces the influence of noisy poses. Then, the multi-head learning module effectively reserves beneficial local temporal dynamics of movement. We also boost discriminative power of person representations by learning body shape cues using a GAT. Experiments on two large-scale VCCRe-ID datasets demonstrate that our proposed framework outperforms state-of-the-art methods by 12.2% in rank-1 accuracy and 7.0% in mAP.